Predictive maintenance

Overview

The Predictive Maintenance project focused on reducing unplanned downtime and improving equipment reliability across an industrial facility. The client relied on reactive and time-based maintenance practices, leading to unexpected failures, production interruptions, and higher maintenance costs.

The objective was to introduce a predictive, data-driven maintenance strategy that would anticipate issues before failures occurred and optimize maintenance planning.

Client challenges

Prior to implementation, the facility faced several maintenance-related issues:

• Unexpected equipment breakdowns impacting production schedules

• Maintenance actions based on fixed intervals rather than actual condition

• Limited visibility into equipment health and performance trends

• High spare parts usage and inefficient maintenance planning

• Difficulty prioritizing maintenance activities across assets

Objectives

The main goals of the project were to:

• Reduce unplanned downtime and production losses

• Shift from reactive to predictive maintenance practices

• Improve equipment reliability and lifespan

• Enable condition-based maintenance decisions

• Optimize maintenance costs and resource allocation

Solution approach

A condition-monitoring and analytics-based approach was implemented to support predictive maintenance. The solution combined sensors, data acquisition, and intelligent analysis integrated with existing automation systems.

Key elements of the solution included:

• Installation of condition monitoring sensors on critical assets

• Continuous data collection for vibration, temperature, and load

• Analytics models to detect anomalies and early failure indicators

• Centralized dashboards for equipment health visibility

• Integration with maintenance planning and alert systems

Implementation process

The project was delivered through a structured and scalable process:

• Asset Assessment & Criticality Analysis
  Identification of high-impact equipment and failure risks to prioritize monitoring efforts.

• System Design
  Development of a predictive maintenance architecture aligned with operational and maintenance workflows.

• Sensor Deployment & Integration
  Installation of sensors and integration with control, data, and maintenance systems.

• Data Validation & Model Tuning
  Verification of data accuracy and refinement of predictive models under real operating conditions.

• Training & Maintenance Enablement
  Training maintenance teams to interpret insights and act proactively on system alerts.

Conclusion

The Predictive Maintenance project transformed maintenance operations from reactive firefighting to proactive asset management. By leveraging real-time condition data and predictive analytics, the client reduced downtime, improved equipment reliability, and established a sustainable maintenance strategy supporting long-term operational excellence.